We aim to enforce some degree
of agreement between the TP predictions of the two networks. For this
reason, we train them jointly and introduce additional constraints in
the loss objective. Similarly to knowledge distillation settings
[1]}, [2]}, we utilize the KL divergence
loss between the screenplay-based posterior distributions
\(q(y_t|\mathcal {D})\)  and the video-based ones \(p(y_t|\mathcal {F})\) .
